A lossless compression scheme has been proposed by [1]}, [2]}, [3]} for the collaborative tasks where the observations of the distributed decision makers are generated by a (Decentralized) partially observable Markov decision process (Dec-)POMDP. Authors suggest an optimal clustering scheme to partition the history of observations of an agent such that no loss in the team objective occurs. While the main intention of the paper is to reduce the complexity of distributed computations done at each individual decision maker, the proposed algorithm can have substantial applications in data compression when a group of collaborative decision makers intend to communicate through rate-limited channels.
Similar to these works, [4]} also proposes k-median clustering to find a proper abstraction of agents' observations before they are communicated to other agents through a communication channel. It is shown that the problem of agents' observation abstraction in a multiagent setting is, in fact, a generalized version of rate-distortion problem.
